A hybrid classification method with dual-channel CNN and KELM for hyperspectral remote sensing images

Zhao, Huimin; Wang, Chenchen; Chen, Huayue; Chen, Tao

doi:10.1080/01431161.2022.2162352

Cited by 4 publications

(1 citation statement)

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“…In fact, 3-D CNNs are just the most direct ways of spectral-spatial feature extraction, and there are some other approaches. Zhao et al [26] developed two kinds of 1-D CNNs to extract spectral and spatial features and then fused these features. Zhang et al [27] combined 1-D CNN with 2-D CNN to exploit spectral-spatial features efficiently.…”

Section: Introductionmentioning

confidence: 99%

A U-Shaped Convolution-Aided Transformer with Double Attention for Hyperspectral Image Classification

Qin,

Wang,

et al. 2024

Remote Sensing

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Convolutional neural networks (CNNs) and transformers have achieved great success in hyperspectral image (HSI) classification. However, CNNs are inefficient in establishing long-range dependencies, and transformers may overlook some local information. To overcome these limitations, we propose a U-shaped convolution-aided transformer (UCaT) that incorporates convolutions into a novel transformer architecture to aid classification. The group convolution is employed as parallel local descriptors to extract detailed features, and then the multi-head self-attention recalibrates these features in consistent groups, emphasizing informative features while maintaining the inherent spectral–spatial data structure. Specifically, three components are constructed using particular strategies. First, the spectral groupwise self-attention (spectral-GSA) component is developed for spectral attention, which selectively emphasizes diagnostic spectral features among neighboring bands and reduces the spectral dimension. Then, the spatial dual-scale convolution-aided self-attention (spatial-DCSA) encoder and spatial convolution-aided cross-attention (spatial-CCA) decoder form a U-shaped architecture for per-pixel classifications over HSI patches, where the encoder utilizes a dual-scale strategy to explore information in different scales and the decoder adopts the cross-attention for information fusion. Experimental results on three datasets demonstrate that the proposed UCaT outperforms the competitors. Additionally, a visual explanation of the UCaT is given, showing its ability to build global interactions and capture pixel-level dependencies.

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Section: Introductionmentioning

confidence: 99%